Caching Parameter Metrics for Data Element Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data caching solutions lack a systematic approach to determine which data elements to cache, leading to subjective decisions based on qualitative analysis, which can result in inefficient resource utilization and degraded system performance.
Innovation Solution
A computer-implemented method that maps non-functional requirements of a system to resource utilization and system performance metric values for caching parameters, using automated code analysis and decision models to identify data elements suitable for caching, thereby improving system performance and throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated code analysis and metric mapping are implemented to determine caching parameters, then data-based caching decisions improve system performance and resource utilization, but the complexity of the caching management system increases
Solution Approach 1:
The system performs automated code analysis and automatically maps non-functional requirements to caching parameters without requiring manual intervention. The computer autonomously identifies data elements suitable for caching and generates caching decisions based on the analysis, allowing the system to self-optimize its caching strategy
Solution Approach 2:
The patent replaces subjective manual analysis with automated computational analysis. Instead of relying on human experts to qualitatively assess which data to cache, the system uses automated code analysis and metric mapping to objectively determine caching parameters, substituting mechanical human decision-making with computational processes
2Ease of manufacture
If subjective qualitative analysis is used to determine caching decisions, then the system is easier to implement, but resource utilization becomes inefficient and system performance degrades
Solution Approach 1:
The system replaces subjective qualitative analysis with automated code analysis and objective metric mapping. The computer automatically analyzes the codebase, maps non-functional requirements to caching parameters, and generates data-driven caching decisions, eliminating the need for subjective human judgment while improving system throughput
Solution Approach 2:
The patent transforms the caching decision process from subjective qualitative assessment to objective quantitative parameter-based decisions. By mapping non-functional requirements to specific caching parameters and using automated analysis, the system changes the nature of decision-making from intuitive to parameter-driven, improving resource utilization and system performance
Data Source
AI summary
Managing data element caching is provided. Non-functional requirements of a system running an application are mapped to resource utilization and system performance metric values corresponding to each of a plurality of caching parameters for each of data elements corresponding to the application suitable for caching. A caching decision is generated for each of the data elements corresponding to the application suitable for caching by identifying certain ones of the data elements for the caching to improve at least one of performance and throughput of the system based on the mapping. A data element caching decision recommendation is generated for the application based on the caching decision. The data element caching decision recommendation corresponding to the application is output to a client device of a customer via a network.